Predictive Autoscaling Of Microservices Hosted In Fog Microdata Center

Fog computing provides microdata center (MDC) facilities closer to the users and applications, which help to overcome the application latency and response time concerns. However, guaranteeing specific service-level objectives (SLOs) for the applications running on the MDC requires automatic scaling of allocated resources by efficiently utilizing the available infrastructure capacity. In this article, we propose a novel predictive autoscaling method for microservices running on the fog MDC to satisfy the application response time SLO. Initially, our proposed approach uses a reactive rule-based autoscaling method to gather the training dataset for building the predictive autoscaling model. The proposed approach is efficient, as it can learn the predictive autoscaling model using an increasing synthetic workload. The learned predictive autoscaling model is used to manage the application resources serving different realistic workloads effectively. Our experimental evaluation using two synthetic and three realistic workloads for two benchmark microservice applications on a real MDC shows excellent performance compared to the existing state-of-the-art baseline rule-based autoscaling method. The proposed autoscaling method yields 75.51% reduction in the number of rejected requests and 77.53% fewer number of SLO violations compared to the baseline autoscaling methods by using only 9.20% additional data center resources at the fog layer.